Asteroid_Hazard_Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Pianomello/Summer-camp-2024/blob/main/Asteroid_Hazard_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yUiUufa-ACDx"
      },
      "source": [
        "<h1><b> Machine Learning in Astronomy : Asteroid Hazardousness"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOl-UzRtACDy"
      },
      "source": [
        "![Asteroid](data:image/jpeg;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5a1RH2W7YJUp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "38Ujz_3HACDz"
      },
      "source": [
        "<h2> Step 1: Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-9XRw6nACD0"
      },
      "source": [
        "<h3><u> Importing useful libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0DFwbR2LACD0"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MehAmgDRACD0"
      },
      "source": [
        "<h3><u> Loading data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5B0yABdtACD0",
        "outputId": "dd4b9368-7307-4cfd-a250-a20bdc0ea264"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Object Name</th>\n",
              "      <th>Epoch (TDB)</th>\n",
              "      <th>Orbit Axis (AU)</th>\n",
              "      <th>Orbit Eccentricity</th>\n",
              "      <th>Orbit Inclination (deg)</th>\n",
              "      <th>Perihelion Argument (deg)</th>\n",
              "      <th>Node Longitude (deg)</th>\n",
              "      <th>Mean Anomoly (deg)</th>\n",
              "      <th>Perihelion Distance (AU)</th>\n",
              "      <th>Aphelion Distance (AU)</th>\n",
              "      <th>Orbital Period (yr)</th>\n",
              "      <th>Minimum Orbit Intersection Distance (AU)</th>\n",
              "      <th>Orbital Reference</th>\n",
              "      <th>Asteroid Magnitude</th>\n",
              "      <th>Classification</th>\n",
              "      <th>Hazardous</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>433 Eros</td>\n",
              "      <td>57800</td>\n",
              "      <td>1.4579</td>\n",
              "      <td>0.2226</td>\n",
              "      <td>10.8277</td>\n",
              "      <td>178.8050</td>\n",
              "      <td>304.3265</td>\n",
              "      <td>319.3111</td>\n",
              "      <td>1.1335</td>\n",
              "      <td>1.78</td>\n",
              "      <td>1.76</td>\n",
              "      <td>0.1492</td>\n",
              "      <td>598</td>\n",
              "      <td>11.16</td>\n",
              "      <td>Amor Asteroid</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>719 Albert</td>\n",
              "      <td>57800</td>\n",
              "      <td>2.6385</td>\n",
              "      <td>0.5479</td>\n",
              "      <td>11.5822</td>\n",
              "      <td>156.1409</td>\n",
              "      <td>183.9204</td>\n",
              "      <td>224.5535</td>\n",
              "      <td>1.1928</td>\n",
              "      <td>4.08</td>\n",
              "      <td>4.29</td>\n",
              "      <td>0.2004</td>\n",
              "      <td>78</td>\n",
              "      <td>15.50</td>\n",
              "      <td>Amor Asteroid</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>887 Alinda</td>\n",
              "      <td>57800</td>\n",
              "      <td>2.4787</td>\n",
              "      <td>0.5671</td>\n",
              "      <td>9.3561</td>\n",
              "      <td>350.3482</td>\n",
              "      <td>110.5444</td>\n",
              "      <td>351.3730</td>\n",
              "      <td>1.0731</td>\n",
              "      <td>3.88</td>\n",
              "      <td>3.90</td>\n",
              "      <td>0.0925</td>\n",
              "      <td>188</td>\n",
              "      <td>13.40</td>\n",
              "      <td>Amor Asteroid</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1036 Ganymed</td>\n",
              "      <td>57800</td>\n",
              "      <td>2.6628</td>\n",
              "      <td>0.5338</td>\n",
              "      <td>26.6929</td>\n",
              "      <td>132.4690</td>\n",
              "      <td>215.5551</td>\n",
              "      <td>92.5640</td>\n",
              "      <td>1.2413</td>\n",
              "      <td>4.08</td>\n",
              "      <td>4.35</td>\n",
              "      <td>0.3421</td>\n",
              "      <td>597</td>\n",
              "      <td>9.45</td>\n",
              "      <td>Amor Asteroid</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1221 Amor</td>\n",
              "      <td>57800</td>\n",
              "      <td>1.9191</td>\n",
              "      <td>0.4356</td>\n",
              "      <td>11.8795</td>\n",
              "      <td>26.6572</td>\n",
              "      <td>171.3448</td>\n",
              "      <td>313.7379</td>\n",
              "      <td>1.0832</td>\n",
              "      <td>2.76</td>\n",
              "      <td>2.66</td>\n",
              "      <td>0.1068</td>\n",
              "      <td>70</td>\n",
              "      <td>17.70</td>\n",
              "      <td>Amor Asteroid</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1566 Icarus</td>\n",
              "      <td>57800</td>\n",
              "      <td>1.0781</td>\n",
              "      <td>0.8269</td>\n",
              "      <td>22.8255</td>\n",
              "      <td>31.3830</td>\n",
              "      <td>88.0107</td>\n",
              "      <td>215.5288</td>\n",
              "      <td>0.1867</td>\n",
              "      <td>1.97</td>\n",
              "      <td>1.12</td>\n",
              "      <td>0.0345</td>\n",
              "      <td>100</td>\n",
              "      <td>16.90</td>\n",
              "      <td>Apollo Asteroid</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1580 Betulia</td>\n",
              "      <td>57800</td>\n",
              "      <td>2.1973</td>\n",
              "      <td>0.4875</td>\n",
              "      <td>52.0908</td>\n",
              "      <td>159.4852</td>\n",
              "      <td>62.2921</td>\n",
              "      <td>191.9105</td>\n",
              "      <td>1.1260</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.1361</td>\n",
              "      <td>191</td>\n",
              "      <td>14.80</td>\n",
              "      <td>Amor Asteroid</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1620 Geographos</td>\n",
              "      <td>57800</td>\n",
              "      <td>1.2453</td>\n",
              "      <td>0.3353</td>\n",
              "      <td>13.3375</td>\n",
              "      <td>276.8930</td>\n",
              "      <td>337.2080</td>\n",
              "      <td>104.1556</td>\n",
              "      <td>0.8277</td>\n",
              "      <td>1.66</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.0307</td>\n",
              "      <td>449</td>\n",
              "      <td>15.60</td>\n",
              "      <td>Apollo Asteroid</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1627 Ivar</td>\n",
              "      <td>57800</td>\n",
              "      <td>1.8628</td>\n",
              "      <td>0.3966</td>\n",
              "      <td>8.4512</td>\n",
              "      <td>167.7730</td>\n",
              "      <td>133.1445</td>\n",
              "      <td>151.4603</td>\n",
              "      <td>1.1241</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.54</td>\n",
              "      <td>0.1124</td>\n",
              "      <td>635</td>\n",
              "      <td>13.20</td>\n",
              "      <td>Amor Asteroid</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1685 Toro</td>\n",
              "      <td>54520</td>\n",
              "      <td>1.3673</td>\n",
              "      <td>0.4359</td>\n",
              "      <td>9.3807</td>\n",
              "      <td>127.0826</td>\n",
              "      <td>274.3396</td>\n",
              "      <td>48.6229</td>\n",
              "      <td>0.7713</td>\n",
              "      <td>1.96</td>\n",
              "      <td>1.60</td>\n",
              "      <td>0.0504</td>\n",
              "      <td>355</td>\n",
              "      <td>14.23</td>\n",
              "      <td>Apollo Asteroid</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Object Name  Epoch (TDB)  Orbit Axis (AU)  Orbit Eccentricity  \\\n",
              "0         433 Eros        57800           1.4579              0.2226   \n",
              "1       719 Albert        57800           2.6385              0.5479   \n",
              "2       887 Alinda        57800           2.4787              0.5671   \n",
              "3     1036 Ganymed        57800           2.6628              0.5338   \n",
              "4        1221 Amor        57800           1.9191              0.4356   \n",
              "5      1566 Icarus        57800           1.0781              0.8269   \n",
              "6     1580 Betulia        57800           2.1973              0.4875   \n",
              "7  1620 Geographos        57800           1.2453              0.3353   \n",
              "8        1627 Ivar        57800           1.8628              0.3966   \n",
              "9        1685 Toro        54520           1.3673              0.4359   \n",
              "\n",
              "   Orbit Inclination (deg)  Perihelion Argument (deg)  Node Longitude (deg)  \\\n",
              "0                  10.8277                   178.8050              304.3265   \n",
              "1                  11.5822                   156.1409              183.9204   \n",
              "2                   9.3561                   350.3482              110.5444   \n",
              "3                  26.6929                   132.4690              215.5551   \n",
              "4                  11.8795                    26.6572              171.3448   \n",
              "5                  22.8255                    31.3830               88.0107   \n",
              "6                  52.0908                   159.4852               62.2921   \n",
              "7                  13.3375                   276.8930              337.2080   \n",
              "8                   8.4512                   167.7730              133.1445   \n",
              "9                   9.3807                   127.0826              274.3396   \n",
              "\n",
              "   Mean Anomoly (deg)  Perihelion Distance (AU)  Aphelion Distance (AU)  \\\n",
              "0            319.3111                    1.1335                    1.78   \n",
              "1            224.5535                    1.1928                    4.08   \n",
              "2            351.3730                    1.0731                    3.88   \n",
              "3             92.5640                    1.2413                    4.08   \n",
              "4            313.7379                    1.0832                    2.76   \n",
              "5            215.5288                    0.1867                    1.97   \n",
              "6            191.9105                    1.1260                    3.27   \n",
              "7            104.1556                    0.8277                    1.66   \n",
              "8            151.4603                    1.1241                    2.60   \n",
              "9             48.6229                    0.7713                    1.96   \n",
              "\n",
              "   Orbital Period (yr)  Minimum Orbit Intersection Distance (AU)  \\\n",
              "0                 1.76                                    0.1492   \n",
              "1                 4.29                                    0.2004   \n",
              "2                 3.90                                    0.0925   \n",
              "3                 4.35                                    0.3421   \n",
              "4                 2.66                                    0.1068   \n",
              "5                 1.12                                    0.0345   \n",
              "6                 3.26                                    0.1361   \n",
              "7                 1.39                                    0.0307   \n",
              "8                 2.54                                    0.1124   \n",
              "9                 1.60                                    0.0504   \n",
              "\n",
              "   Orbital Reference  Asteroid Magnitude   Classification  Hazardous  \n",
              "0                598               11.16    Amor Asteroid      False  \n",
              "1                 78               15.50    Amor Asteroid      False  \n",
              "2                188               13.40    Amor Asteroid      False  \n",
              "3                597                9.45    Amor Asteroid      False  \n",
              "4                 70               17.70    Amor Asteroid      False  \n",
              "5                100               16.90  Apollo Asteroid       True  \n",
              "6                191               14.80    Amor Asteroid      False  \n",
              "7                449               15.60  Apollo Asteroid       True  \n",
              "8                635               13.20    Amor Asteroid      False  \n",
              "9                355               14.23  Apollo Asteroid      False  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = pd.read_csv('Asteroid-Data.csv')\n",
        "data.head(10)\n",
        "#data.sample(10)\n",
        "#data.tail(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vnhFt1dACD1"
      },
      "source": [
        "<h2> Step 2: Data Analysis and Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_BtrtF5oACD1"
      },
      "source": [
        "\n",
        "| **Feature**                              | **Description**                                                                                                                                                                                                                      |\n",
        "|------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
        "| **Object Name**                          | The name or designation of the asteroid.                                                                                                                                                                                             |\n",
        "| **Epoch (TDB)**                          | The specific date and time for which the orbital elements are calculated, given in Barycentric Dynamical Time (TDB).                                                                                                                 |\n",
        "| **Orbit Axis (AU)**                      | The semi-major axis of the asteroid's orbit, measured in astronomical units (AU). This is the average distance between the asteroid and the Sun over one complete orbit.                                                              |\n",
        "| **Orbit Eccentricity**                   | A measure of the deviation of the orbit from a perfect circle. An eccentricity of 0 indicates a circular orbit, while values closer to 1 indicate more elongated orbits.                                                              |\n",
        "| **Orbit Inclination (deg)**              | The tilt of the asteroid's orbit relative to the plane of the solar system, measured in degrees.                                                                                                                                      |\n",
        "| **Perihelion Argument (deg)**            | The angle from the ascending node to the perihelion, measured in the plane of the asteroid's orbit, in degrees. This indicates the orientation of the ellipse within the orbital plane.                                               |\n",
        "| **Node Longitude (deg)**                 | The angle from the reference direction (usually the vernal equinox) to the ascending node of the orbit, measured in degrees. The ascending node is the point where the asteroid crosses the ecliptic plane from South to North.       |\n",
        "| **Mean Anomaly (deg)**                   | The fraction of the orbital period that has elapsed since the last perihelion, expressed in degrees. This helps in calculating the position of the asteroid along its orbit at a specific time.                                         |\n",
        "| **Perihelion Distance (AU)**             | The closest distance between the asteroid and the Sun, measured in astronomical units (AU).                                                                                                                                           |\n",
        "| **Aphelion Distance (AU)**               | The farthest distance between the asteroid and the Sun, measured in astronomical units (AU).                                                                                                                                          |\n",
        "| **Orbital Period (yr)**                  | The time it takes for the asteroid to complete one full orbit around the Sun, measured in years.                                                                                                                                      |\n",
        "| **Minimum Orbit Intersection Distance (AU)** | The closest distance between the orbit of the asteroid and the orbit of Earth, measured in astronomical units (AU).                                                                                                                    |\n",
        "| **Orbital Reference**                    | The reference system or dataset used to calculate the orbital elements.                                                                                                                                                               |\n",
        "| **Asteroid Magnitude**                   | The brightness of the asteroid as seen from Earth, typically measured on the absolute magnitude scale.                                                                                                                                |\n",
        "| **Classification**                       | The type or category of the asteroid based on its orbit and other characteristics. Aopllo/Aten/ Apohele/Amor                                                                                 |\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x9M_2Vy2ACD1",
        "outputId": "67b6b67d-3a65-4571-8e29-8bf80d7f5acc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15635 entries, 0 to 15634\n",
            "Data columns (total 16 columns):\n",
            " #   Column                                    Non-Null Count  Dtype  \n",
            "---  ------                                    --------------  -----  \n",
            " 0   Object Name                               15635 non-null  object \n",
            " 1   Epoch (TDB)                               15635 non-null  int64  \n",
            " 2   Orbit Axis (AU)                           15635 non-null  float64\n",
            " 3   Orbit Eccentricity                        15635 non-null  float64\n",
            " 4   Orbit Inclination (deg)                   15635 non-null  float64\n",
            " 5   Perihelion Argument (deg)                 15635 non-null  float64\n",
            " 6   Node Longitude (deg)                      15635 non-null  float64\n",
            " 7   Mean Anomoly (deg)                        15635 non-null  float64\n",
            " 8   Perihelion Distance (AU)                  15635 non-null  float64\n",
            " 9   Aphelion Distance (AU)                    15635 non-null  float64\n",
            " 10  Orbital Period (yr)                       15635 non-null  float64\n",
            " 11  Minimum Orbit Intersection Distance (AU)  15635 non-null  float64\n",
            " 12  Orbital Reference                         15635 non-null  int64  \n",
            " 13  Asteroid Magnitude                        15634 non-null  float64\n",
            " 14  Classification                            15635 non-null  object \n",
            " 15  Hazardous                                 15635 non-null  bool   \n",
            "dtypes: bool(1), float64(11), int64(2), object(2)\n",
            "memory usage: 1.8+ MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDCNvSysACD2"
      },
      "source": [
        "<h3><u> Checking missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8w95xSbrACD2",
        "outputId": "8c7416f6-4907-47ad-9178-d1a3e0650a57"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Object Name                                 0\n",
              "Epoch (TDB)                                 0\n",
              "Orbit Axis (AU)                             0\n",
              "Orbit Eccentricity                          0\n",
              "Orbit Inclination (deg)                     0\n",
              "Perihelion Argument (deg)                   0\n",
              "Node Longitude (deg)                        0\n",
              "Mean Anomoly (deg)                          0\n",
              "Perihelion Distance (AU)                    0\n",
              "Aphelion Distance (AU)                      0\n",
              "Orbital Period (yr)                         0\n",
              "Minimum Orbit Intersection Distance (AU)    0\n",
              "Orbital Reference                           0\n",
              "Asteroid Magnitude                          1\n",
              "Classification                              0\n",
              "Hazardous                                   0\n",
              "dtype: int64"
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          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LKNPnVCcACD2",
        "outputId": "86053a56-bc4e-4c93-cd7f-8d941349b9fc"
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      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>Object Name</th>\n",
              "      <th>Epoch (TDB)</th>\n",
              "      <th>Orbit Axis (AU)</th>\n",
              "      <th>Orbit Eccentricity</th>\n",
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              "      <th>Aphelion Distance (AU)</th>\n",
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              "      <th>Minimum Orbit Intersection Distance (AU)</th>\n",
              "      <th>Orbital Reference</th>\n",
              "      <th>Asteroid Magnitude</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6896</th>\n",
              "      <td>(2010 AU118)</td>\n",
              "      <td>55211</td>\n",
              "      <td>1.6214</td>\n",
              "      <td>0.3059</td>\n",
              "      <td>43.7254</td>\n",
              "      <td>339.6013</td>\n",
              "      <td>35.2163</td>\n",
              "      <td>109.2828</td>\n",
              "      <td>1.1255</td>\n",
              "      <td>2.12</td>\n",
              "      <td>2.06</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Amor Asteroid</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Object Name  Epoch (TDB)  Orbit Axis (AU)  Orbit Eccentricity  \\\n",
              "6896  (2010 AU118)        55211           1.6214              0.3059   \n",
              "\n",
              "      Orbit Inclination (deg)  Perihelion Argument (deg)  \\\n",
              "6896                  43.7254                   339.6013   \n",
              "\n",
              "      Node Longitude (deg)  Mean Anomoly (deg)  Perihelion Distance (AU)  \\\n",
              "6896               35.2163            109.2828                    1.1255   \n",
              "\n",
              "      Aphelion Distance (AU)  Orbital Period (yr)  \\\n",
              "6896                    2.12                 2.06   \n",
              "\n",
              "      Minimum Orbit Intersection Distance (AU)  Orbital Reference  \\\n",
              "6896                                    0.1467                  7   \n",
              "\n",
              "      Asteroid Magnitude Classification  Hazardous  \n",
              "6896                 NaN  Amor Asteroid      False  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "missing_value = data[data['Asteroid Magnitude'].isnull()]\n",
        "missing_value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9vwT9_TQACD2",
        "outputId": "b360dff9-816f-4bba-e539-90883ddca447"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Object Name                                 0\n",
              "Epoch (TDB)                                 0\n",
              "Orbit Axis (AU)                             0\n",
              "Orbit Eccentricity                          0\n",
              "Orbit Inclination (deg)                     0\n",
              "Perihelion Argument (deg)                   0\n",
              "Node Longitude (deg)                        0\n",
              "Mean Anomoly (deg)                          0\n",
              "Perihelion Distance (AU)                    0\n",
              "Aphelion Distance (AU)                      0\n",
              "Orbital Period (yr)                         0\n",
              "Minimum Orbit Intersection Distance (AU)    0\n",
              "Orbital Reference                           0\n",
              "Asteroid Magnitude                          0\n",
              "Classification                              0\n",
              "Hazardous                                   0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = data.dropna()\n",
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3AZHtzLACD2"
      },
      "source": [
        "<h3><u> Checking duplicate values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I5qNWzd0ACD2",
        "outputId": "dd8a90ba-f2d8-4a12-ec73-dbfec22923f6"
      },
      "outputs": [
        {
          "data": {
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              "      <th>Object Name</th>\n",
              "      <th>Epoch (TDB)</th>\n",
              "      <th>Orbit Axis (AU)</th>\n",
              "      <th>Orbit Eccentricity</th>\n",
              "      <th>Orbit Inclination (deg)</th>\n",
              "      <th>Perihelion Argument (deg)</th>\n",
              "      <th>Node Longitude (deg)</th>\n",
              "      <th>Mean Anomoly (deg)</th>\n",
              "      <th>Perihelion Distance (AU)</th>\n",
              "      <th>Aphelion Distance (AU)</th>\n",
              "      <th>Orbital Period (yr)</th>\n",
              "      <th>Minimum Orbit Intersection Distance (AU)</th>\n",
              "      <th>Orbital Reference</th>\n",
              "      <th>Asteroid Magnitude</th>\n",
              "      <th>Classification</th>\n",
              "      <th>Hazardous</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Object Name, Epoch (TDB), Orbit Axis (AU), Orbit Eccentricity, Orbit Inclination (deg), Perihelion Argument (deg), Node Longitude (deg), Mean Anomoly (deg), Perihelion Distance (AU), Aphelion Distance (AU), Orbital Period (yr), Minimum Orbit Intersection Distance (AU), Orbital Reference, Asteroid Magnitude, Classification, Hazardous]\n",
              "Index: []"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "duplicate_data = data[data.duplicated()]\n",
        "duplicate_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xyBhg5HJACD3",
        "outputId": "07a57b6b-55c5-4507-ca87-a4e6adb99847"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Epoch (TDB)</th>\n",
              "      <th>Orbit Axis (AU)</th>\n",
              "      <th>Orbit Eccentricity</th>\n",
              "      <th>Orbit Inclination (deg)</th>\n",
              "      <th>Perihelion Argument (deg)</th>\n",
              "      <th>Node Longitude (deg)</th>\n",
              "      <th>Mean Anomoly (deg)</th>\n",
              "      <th>Perihelion Distance (AU)</th>\n",
              "      <th>Aphelion Distance (AU)</th>\n",
              "      <th>Orbital Period (yr)</th>\n",
              "      <th>Minimum Orbit Intersection Distance (AU)</th>\n",
              "      <th>Orbital Reference</th>\n",
              "      <th>Asteroid Magnitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>57365.532749</td>\n",
              "      <td>1.781012</td>\n",
              "      <td>0.449703</td>\n",
              "      <td>12.939206</td>\n",
              "      <td>181.499448</td>\n",
              "      <td>172.680946</td>\n",
              "      <td>172.838928</td>\n",
              "      <td>0.914899</td>\n",
              "      <td>2.647120</td>\n",
              "      <td>2.473433</td>\n",
              "      <td>0.101881</td>\n",
              "      <td>21.886210</td>\n",
              "      <td>22.286336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1167.148181</td>\n",
              "      <td>0.603247</td>\n",
              "      <td>0.176002</td>\n",
              "      <td>11.300910</td>\n",
              "      <td>103.858105</td>\n",
              "      <td>103.381604</td>\n",
              "      <td>116.125667</td>\n",
              "      <td>0.240310</td>\n",
              "      <td>1.142688</td>\n",
              "      <td>1.517633</td>\n",
              "      <td>0.105940</td>\n",
              "      <td>33.632956</td>\n",
              "      <td>3.016002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>44221.000000</td>\n",
              "      <td>0.547600</td>\n",
              "      <td>0.004400</td>\n",
              "      <td>0.014700</td>\n",
              "      <td>0.008100</td>\n",
              "      <td>0.007000</td>\n",
              "      <td>0.003100</td>\n",
              "      <td>0.070700</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>57800.000000</td>\n",
              "      <td>1.323600</td>\n",
              "      <td>0.322725</td>\n",
              "      <td>4.762175</td>\n",
              "      <td>92.432975</td>\n",
              "      <td>82.248100</td>\n",
              "      <td>62.030375</td>\n",
              "      <td>0.776725</td>\n",
              "      <td>1.740000</td>\n",
              "      <td>1.520000</td>\n",
              "      <td>0.018700</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>19.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>57800.000000</td>\n",
              "      <td>1.735500</td>\n",
              "      <td>0.462950</td>\n",
              "      <td>9.196350</td>\n",
              "      <td>183.300400</td>\n",
              "      <td>172.180350</td>\n",
              "      <td>167.750600</td>\n",
              "      <td>0.965800</td>\n",
              "      <td>2.520000</td>\n",
              "      <td>2.290000</td>\n",
              "      <td>0.062000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>22.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>57800.000000</td>\n",
              "      <td>2.208675</td>\n",
              "      <td>0.572900</td>\n",
              "      <td>18.508175</td>\n",
              "      <td>270.908350</td>\n",
              "      <td>253.664375</td>\n",
              "      <td>281.445200</td>\n",
              "      <td>1.080300</td>\n",
              "      <td>3.420000</td>\n",
              "      <td>3.280000</td>\n",
              "      <td>0.159175</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>24.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>57800.000000</td>\n",
              "      <td>21.395400</td>\n",
              "      <td>0.969500</td>\n",
              "      <td>154.375100</td>\n",
              "      <td>359.994200</td>\n",
              "      <td>359.998100</td>\n",
              "      <td>359.998200</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>41.540000</td>\n",
              "      <td>98.970000</td>\n",
              "      <td>0.706900</td>\n",
              "      <td>635.000000</td>\n",
              "      <td>33.200000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Epoch (TDB)  Orbit Axis (AU)  Orbit Eccentricity  \\\n",
              "count  15634.000000     15634.000000        15634.000000   \n",
              "mean   57365.532749         1.781012            0.449703   \n",
              "std     1167.148181         0.603247            0.176002   \n",
              "min    44221.000000         0.547600            0.004400   \n",
              "25%    57800.000000         1.323600            0.322725   \n",
              "50%    57800.000000         1.735500            0.462950   \n",
              "75%    57800.000000         2.208675            0.572900   \n",
              "max    57800.000000        21.395400            0.969500   \n",
              "\n",
              "       Orbit Inclination (deg)  Perihelion Argument (deg)  \\\n",
              "count             15634.000000               15634.000000   \n",
              "mean                 12.939206                 181.499448   \n",
              "std                  11.300910                 103.858105   \n",
              "min                   0.014700                   0.008100   \n",
              "25%                   4.762175                  92.432975   \n",
              "50%                   9.196350                 183.300400   \n",
              "75%                  18.508175                 270.908350   \n",
              "max                 154.375100                 359.994200   \n",
              "\n",
              "       Node Longitude (deg)  Mean Anomoly (deg)  Perihelion Distance (AU)  \\\n",
              "count          15634.000000        15634.000000              15634.000000   \n",
              "mean             172.680946          172.838928                  0.914899   \n",
              "std              103.381604          116.125667                  0.240310   \n",
              "min                0.007000            0.003100                  0.070700   \n",
              "25%               82.248100           62.030375                  0.776725   \n",
              "50%              172.180350          167.750600                  0.965800   \n",
              "75%              253.664375          281.445200                  1.080300   \n",
              "max              359.998100          359.998200                  1.300000   \n",
              "\n",
              "       Aphelion Distance (AU)  Orbital Period (yr)  \\\n",
              "count            15634.000000         15634.000000   \n",
              "mean                 2.647120             2.473433   \n",
              "std                  1.142688             1.517633   \n",
              "min                  0.800000             0.410000   \n",
              "25%                  1.740000             1.520000   \n",
              "50%                  2.520000             2.290000   \n",
              "75%                  3.420000             3.280000   \n",
              "max                 41.540000            98.970000   \n",
              "\n",
              "       Minimum Orbit Intersection Distance (AU)  Orbital Reference  \\\n",
              "count                              15634.000000       15634.000000   \n",
              "mean                                   0.101881          21.886210   \n",
              "std                                    0.105940          33.632956   \n",
              "min                                    0.000000           1.000000   \n",
              "25%                                    0.018700           6.000000   \n",
              "50%                                    0.062000          11.000000   \n",
              "75%                                    0.159175          23.000000   \n",
              "max                                    0.706900         635.000000   \n",
              "\n",
              "       Asteroid Magnitude  \n",
              "count        15634.000000  \n",
              "mean            22.286336  \n",
              "std              3.016002  \n",
              "min              9.450000  \n",
              "25%             19.900000  \n",
              "50%             22.200000  \n",
              "75%             24.600000  \n",
              "max             33.200000  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzLQFBJAACD3"
      },
      "source": [
        "<h3><u> Converting categorical features into numerical"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ckLYXkiGACD3"
      },
      "outputs": [],
      "source": [
        "data['Hazardous'] = [0 if value == False else 1 for value in data['Hazardous']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XqjnbaZUACD3",
        "outputId": "fbe19c68-2dea-492d-d689-790594a30a3a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Hazardous Count'}, xlabel='Hazardous', ylabel='count'>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.title('Hazardous Count')\n",
        "sns.countplot(data=data, x ='Hazardous')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_N31JooPACD3",
        "outputId": "05a6bdbd-70cc-4281-98be-3a1d55a77e38"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Classification Count colored by Hazardous'}, xlabel='Classification', ylabel='count'>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.title('Classification Count colored by Hazardous')\n",
        "sns.countplot(data=data, x='Classification', hue='Hazardous')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_29bwBAUACD3"
      },
      "source": [
        "![Asteroid Classification](https://www.sott.net/image/s12/249216/full/orbits.jpg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Tenl3v0ACD3"
      },
      "outputs": [],
      "source": [
        "data['Classification'] = [0 if value == 'Amor Asteroid'\n",
        "                        else 1 if value == 'Apollo Asteroid'\n",
        "                        else 2 if value == 'Aten Asteroid'\n",
        "                        else 3\n",
        "                        for value in data['Classification']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uDsJsA-DACD3",
        "outputId": "00be38b8-fde2-4c7d-972a-b6d14c3328b4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Object Name</th>\n",
              "      <th>Epoch (TDB)</th>\n",
              "      <th>Orbit Axis (AU)</th>\n",
              "      <th>Orbit Eccentricity</th>\n",
              "      <th>Orbit Inclination (deg)</th>\n",
              "      <th>Perihelion Argument (deg)</th>\n",
              "      <th>Node Longitude (deg)</th>\n",
              "      <th>Mean Anomoly (deg)</th>\n",
              "      <th>Perihelion Distance (AU)</th>\n",
              "      <th>Aphelion Distance (AU)</th>\n",
              "      <th>Orbital Period (yr)</th>\n",
              "      <th>Minimum Orbit Intersection Distance (AU)</th>\n",
              "      <th>Orbital Reference</th>\n",
              "      <th>Asteroid Magnitude</th>\n",
              "      <th>Classification</th>\n",
              "      <th>Hazardous</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3273</th>\n",
              "      <td>(2002 XO1)</td>\n",
              "      <td>57800</td>\n",
              "      <td>2.8076</td>\n",
              "      <td>0.5757</td>\n",
              "      <td>6.4914</td>\n",
              "      <td>323.6543</td>\n",
              "      <td>77.2214</td>\n",
              "      <td>12.9705</td>\n",
              "      <td>1.1913</td>\n",
              "      <td>4.42</td>\n",
              "      <td>4.70</td>\n",
              "      <td>0.2126</td>\n",
              "      <td>12</td>\n",
              "      <td>20.7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6861</th>\n",
              "      <td>(2009 YU6)</td>\n",
              "      <td>57800</td>\n",
              "      <td>2.4509</td>\n",
              "      <td>0.6311</td>\n",
              "      <td>12.1679</td>\n",
              "      <td>295.0542</td>\n",
              "      <td>91.6904</td>\n",
              "      <td>327.4553</td>\n",
              "      <td>0.9041</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.84</td>\n",
              "      <td>0.0798</td>\n",
              "      <td>8</td>\n",
              "      <td>21.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1275</th>\n",
              "      <td>327140 (2005 EE224)</td>\n",
              "      <td>57800</td>\n",
              "      <td>1.7447</td>\n",
              "      <td>0.3735</td>\n",
              "      <td>21.8258</td>\n",
              "      <td>208.2541</td>\n",
              "      <td>19.9821</td>\n",
              "      <td>33.7667</td>\n",
              "      <td>1.0930</td>\n",
              "      <td>2.40</td>\n",
              "      <td>2.30</td>\n",
              "      <td>0.1194</td>\n",
              "      <td>21</td>\n",
              "      <td>19.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2508</th>\n",
              "      <td>(1998 MW5)</td>\n",
              "      <td>57800</td>\n",
              "      <td>1.0233</td>\n",
              "      <td>0.3627</td>\n",
              "      <td>6.2964</td>\n",
              "      <td>26.6235</td>\n",
              "      <td>80.4048</td>\n",
              "      <td>150.5771</td>\n",
              "      <td>0.6521</td>\n",
              "      <td>1.39</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.0764</td>\n",
              "      <td>12</td>\n",
              "      <td>19.1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5854</th>\n",
              "      <td>(2008 PV16)</td>\n",
              "      <td>54693</td>\n",
              "      <td>1.8686</td>\n",
              "      <td>0.5918</td>\n",
              "      <td>33.9352</td>\n",
              "      <td>260.1613</td>\n",
              "      <td>146.2495</td>\n",
              "      <td>337.0828</td>\n",
              "      <td>0.7628</td>\n",
              "      <td>2.97</td>\n",
              "      <td>2.55</td>\n",
              "      <td>0.0659</td>\n",
              "      <td>13</td>\n",
              "      <td>21.3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11293</th>\n",
              "      <td>(2014 OA339)</td>\n",
              "      <td>57800</td>\n",
              "      <td>1.3077</td>\n",
              "      <td>0.3689</td>\n",
              "      <td>1.8705</td>\n",
              "      <td>315.1691</td>\n",
              "      <td>78.7624</td>\n",
              "      <td>206.3009</td>\n",
              "      <td>0.8253</td>\n",
              "      <td>1.79</td>\n",
              "      <td>1.50</td>\n",
              "      <td>0.0116</td>\n",
              "      <td>5</td>\n",
              "      <td>24.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5359</th>\n",
              "      <td>(2007 VM84)</td>\n",
              "      <td>54413</td>\n",
              "      <td>1.3014</td>\n",
              "      <td>0.1917</td>\n",
              "      <td>24.7322</td>\n",
              "      <td>7.5606</td>\n",
              "      <td>44.6651</td>\n",
              "      <td>356.7817</td>\n",
              "      <td>1.0520</td>\n",
              "      <td>1.55</td>\n",
              "      <td>1.48</td>\n",
              "      <td>0.0624</td>\n",
              "      <td>7</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12029</th>\n",
              "      <td>(2015 BE4)</td>\n",
              "      <td>57800</td>\n",
              "      <td>1.7277</td>\n",
              "      <td>0.4945</td>\n",
              "      <td>6.6617</td>\n",
              "      <td>308.1900</td>\n",
              "      <td>112.5436</td>\n",
              "      <td>349.5006</td>\n",
              "      <td>0.8733</td>\n",
              "      <td>2.58</td>\n",
              "      <td>2.27</td>\n",
              "      <td>0.0060</td>\n",
              "      <td>13</td>\n",
              "      <td>23.7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14259</th>\n",
              "      <td>(2016 HE19)</td>\n",
              "      <td>57800</td>\n",
              "      <td>1.2395</td>\n",
              "      <td>0.1342</td>\n",
              "      <td>7.3344</td>\n",
              "      <td>166.4157</td>\n",
              "      <td>47.9933</td>\n",
              "      <td>212.3814</td>\n",
              "      <td>1.0732</td>\n",
              "      <td>1.41</td>\n",
              "      <td>1.38</td>\n",
              "      <td>0.0671</td>\n",
              "      <td>7</td>\n",
              "      <td>26.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9302</th>\n",
              "      <td>(2012 VA5)</td>\n",
              "      <td>57800</td>\n",
              "      <td>2.5985</td>\n",
              "      <td>0.6119</td>\n",
              "      <td>6.0631</td>\n",
              "      <td>36.7071</td>\n",
              "      <td>32.1349</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>1.0084</td>\n",
              "      <td>4.19</td>\n",
              "      <td>4.19</td>\n",
              "      <td>0.0514</td>\n",
              "      <td>21</td>\n",
              "      <td>21.9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Object Name  Epoch (TDB)  Orbit Axis (AU)  Orbit Eccentricity  \\\n",
              "3273            (2002 XO1)        57800           2.8076              0.5757   \n",
              "6861            (2009 YU6)        57800           2.4509              0.6311   \n",
              "1275   327140 (2005 EE224)        57800           1.7447              0.3735   \n",
              "2508            (1998 MW5)        57800           1.0233              0.3627   \n",
              "5854           (2008 PV16)        54693           1.8686              0.5918   \n",
              "11293         (2014 OA339)        57800           1.3077              0.3689   \n",
              "5359           (2007 VM84)        54413           1.3014              0.1917   \n",
              "12029           (2015 BE4)        57800           1.7277              0.4945   \n",
              "14259          (2016 HE19)        57800           1.2395              0.1342   \n",
              "9302            (2012 VA5)        57800           2.5985              0.6119   \n",
              "\n",
              "       Orbit Inclination (deg)  Perihelion Argument (deg)  \\\n",
              "3273                    6.4914                   323.6543   \n",
              "6861                   12.1679                   295.0542   \n",
              "1275                   21.8258                   208.2541   \n",
              "2508                    6.2964                    26.6235   \n",
              "5854                   33.9352                   260.1613   \n",
              "11293                   1.8705                   315.1691   \n",
              "5359                   24.7322                     7.5606   \n",
              "12029                   6.6617                   308.1900   \n",
              "14259                   7.3344                   166.4157   \n",
              "9302                    6.0631                    36.7071   \n",
              "\n",
              "       Node Longitude (deg)  Mean Anomoly (deg)  Perihelion Distance (AU)  \\\n",
              "3273                77.2214             12.9705                    1.1913   \n",
              "6861                91.6904            327.4553                    0.9041   \n",
              "1275                19.9821             33.7667                    1.0930   \n",
              "2508                80.4048            150.5771                    0.6521   \n",
              "5854               146.2495            337.0828                    0.7628   \n",
              "11293               78.7624            206.3009                    0.8253   \n",
              "5359                44.6651            356.7817                    1.0520   \n",
              "12029              112.5436            349.5006                    0.8733   \n",
              "14259               47.9933            212.3814                    1.0732   \n",
              "9302                32.1349              0.6940                    1.0084   \n",
              "\n",
              "       Aphelion Distance (AU)  Orbital Period (yr)  \\\n",
              "3273                     4.42                 4.70   \n",
              "6861                     4.00                 3.84   \n",
              "1275                     2.40                 2.30   \n",
              "2508                     1.39                 1.04   \n",
              "5854                     2.97                 2.55   \n",
              "11293                    1.79                 1.50   \n",
              "5359                     1.55                 1.48   \n",
              "12029                    2.58                 2.27   \n",
              "14259                    1.41                 1.38   \n",
              "9302                     4.19                 4.19   \n",
              "\n",
              "       Minimum Orbit Intersection Distance (AU)  Orbital Reference  \\\n",
              "3273                                     0.2126                 12   \n",
              "6861                                     0.0798                  8   \n",
              "1275                                     0.1194                 21   \n",
              "2508                                     0.0764                 12   \n",
              "5854                                     0.0659                 13   \n",
              "11293                                    0.0116                  5   \n",
              "5359                                     0.0624                  7   \n",
              "12029                                    0.0060                 13   \n",
              "14259                                    0.0671                  7   \n",
              "9302                                     0.0514                 21   \n",
              "\n",
              "       Asteroid Magnitude  Classification  Hazardous  \n",
              "3273                 20.7               0          0  \n",
              "6861                 21.4               1          0  \n",
              "1275                 19.9               0          0  \n",
              "2508                 19.1               1          0  \n",
              "5854                 21.3               1          0  \n",
              "11293                24.4               1          0  \n",
              "5359                 23.3               0          0  \n",
              "12029                23.7               1          0  \n",
              "14259                26.3               0          0  \n",
              "9302                 21.9               1          0  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.sample(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eR5wK91rACD4"
      },
      "outputs": [],
      "source": [
        "data_new = data.drop(columns='Object Name')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sfxA5wJeACD4"
      },
      "outputs": [],
      "source": [
        "X = data_new.drop(columns='Hazardous')\n",
        "y = data_new['Hazardous']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rD38m8DBACD4",
        "outputId": "b217228d-8212-4a93-d4ba-3b1f8d6d6f25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 15634 entries, 0 to 15634\n",
            "Data columns (total 14 columns):\n",
            " #   Column                                    Non-Null Count  Dtype  \n",
            "---  ------                                    --------------  -----  \n",
            " 0   Epoch (TDB)                               15634 non-null  int64  \n",
            " 1   Orbit Axis (AU)                           15634 non-null  float64\n",
            " 2   Orbit Eccentricity                        15634 non-null  float64\n",
            " 3   Orbit Inclination (deg)                   15634 non-null  float64\n",
            " 4   Perihelion Argument (deg)                 15634 non-null  float64\n",
            " 5   Node Longitude (deg)                      15634 non-null  float64\n",
            " 6   Mean Anomoly (deg)                        15634 non-null  float64\n",
            " 7   Perihelion Distance (AU)                  15634 non-null  float64\n",
            " 8   Aphelion Distance (AU)                    15634 non-null  float64\n",
            " 9   Orbital Period (yr)                       15634 non-null  float64\n",
            " 10  Minimum Orbit Intersection Distance (AU)  15634 non-null  float64\n",
            " 11  Orbital Reference                         15634 non-null  int64  \n",
            " 12  Asteroid Magnitude                        15634 non-null  float64\n",
            " 13  Classification                            15634 non-null  int64  \n",
            "dtypes: float64(11), int64(3)\n",
            "memory usage: 1.8 MB\n"
          ]
        }
      ],
      "source": [
        "X.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Znk-5LqqACD4",
        "outputId": "06e5838b-3a8c-41c4-9794-c3bd8fad020d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Epoch (TDB)</th>\n",
              "      <th>Orbit Axis (AU)</th>\n",
              "      <th>Orbit Eccentricity</th>\n",
              "      <th>Orbit Inclination (deg)</th>\n",
              "      <th>Perihelion Argument (deg)</th>\n",
              "      <th>Node Longitude (deg)</th>\n",
              "      <th>Mean Anomoly (deg)</th>\n",
              "      <th>Perihelion Distance (AU)</th>\n",
              "      <th>Aphelion Distance (AU)</th>\n",
              "      <th>Orbital Period (yr)</th>\n",
              "      <th>Minimum Orbit Intersection Distance (AU)</th>\n",
              "      <th>Orbital Reference</th>\n",
              "      <th>Asteroid Magnitude</th>\n",
              "      <th>Classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "      <td>15634.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>57365.532749</td>\n",
              "      <td>1.781012</td>\n",
              "      <td>0.449703</td>\n",
              "      <td>12.939206</td>\n",
              "      <td>181.499448</td>\n",
              "      <td>172.680946</td>\n",
              "      <td>172.838928</td>\n",
              "      <td>0.914899</td>\n",
              "      <td>2.647120</td>\n",
              "      <td>2.473433</td>\n",
              "      <td>0.101881</td>\n",
              "      <td>21.886210</td>\n",
              "      <td>22.286336</td>\n",
              "      <td>0.690290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1167.148181</td>\n",
              "      <td>0.603247</td>\n",
              "      <td>0.176002</td>\n",
              "      <td>11.300910</td>\n",
              "      <td>103.858105</td>\n",
              "      <td>103.381604</td>\n",
              "      <td>116.125667</td>\n",
              "      <td>0.240310</td>\n",
              "      <td>1.142688</td>\n",
              "      <td>1.517633</td>\n",
              "      <td>0.105940</td>\n",
              "      <td>33.632956</td>\n",
              "      <td>3.016002</td>\n",
              "      <td>0.605017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>44221.000000</td>\n",
              "      <td>0.547600</td>\n",
              "      <td>0.004400</td>\n",
              "      <td>0.014700</td>\n",
              "      <td>0.008100</td>\n",
              "      <td>0.007000</td>\n",
              "      <td>0.003100</td>\n",
              "      <td>0.070700</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.450000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>57800.000000</td>\n",
              "      <td>1.323600</td>\n",
              "      <td>0.322725</td>\n",
              "      <td>4.762175</td>\n",
              "      <td>92.432975</td>\n",
              "      <td>82.248100</td>\n",
              "      <td>62.030375</td>\n",
              "      <td>0.776725</td>\n",
              "      <td>1.740000</td>\n",
              "      <td>1.520000</td>\n",
              "      <td>0.018700</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>19.900000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>57800.000000</td>\n",
              "      <td>1.735500</td>\n",
              "      <td>0.462950</td>\n",
              "      <td>9.196350</td>\n",
              "      <td>183.300400</td>\n",
              "      <td>172.180350</td>\n",
              "      <td>167.750600</td>\n",
              "      <td>0.965800</td>\n",
              "      <td>2.520000</td>\n",
              "      <td>2.290000</td>\n",
              "      <td>0.062000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>57800.000000</td>\n",
              "      <td>2.208675</td>\n",
              "      <td>0.572900</td>\n",
              "      <td>18.508175</td>\n",
              "      <td>270.908350</td>\n",
              "      <td>253.664375</td>\n",
              "      <td>281.445200</td>\n",
              "      <td>1.080300</td>\n",
              "      <td>3.420000</td>\n",
              "      <td>3.280000</td>\n",
              "      <td>0.159175</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>24.600000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>57800.000000</td>\n",
              "      <td>21.395400</td>\n",
              "      <td>0.969500</td>\n",
              "      <td>154.375100</td>\n",
              "      <td>359.994200</td>\n",
              "      <td>359.998100</td>\n",
              "      <td>359.998200</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>41.540000</td>\n",
              "      <td>98.970000</td>\n",
              "      <td>0.706900</td>\n",
              "      <td>635.000000</td>\n",
              "      <td>33.200000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Epoch (TDB)  Orbit Axis (AU)  Orbit Eccentricity  \\\n",
              "count  15634.000000     15634.000000        15634.000000   \n",
              "mean   57365.532749         1.781012            0.449703   \n",
              "std     1167.148181         0.603247            0.176002   \n",
              "min    44221.000000         0.547600            0.004400   \n",
              "25%    57800.000000         1.323600            0.322725   \n",
              "50%    57800.000000         1.735500            0.462950   \n",
              "75%    57800.000000         2.208675            0.572900   \n",
              "max    57800.000000        21.395400            0.969500   \n",
              "\n",
              "       Orbit Inclination (deg)  Perihelion Argument (deg)  \\\n",
              "count             15634.000000               15634.000000   \n",
              "mean                 12.939206                 181.499448   \n",
              "std                  11.300910                 103.858105   \n",
              "min                   0.014700                   0.008100   \n",
              "25%                   4.762175                  92.432975   \n",
              "50%                   9.196350                 183.300400   \n",
              "75%                  18.508175                 270.908350   \n",
              "max                 154.375100                 359.994200   \n",
              "\n",
              "       Node Longitude (deg)  Mean Anomoly (deg)  Perihelion Distance (AU)  \\\n",
              "count          15634.000000        15634.000000              15634.000000   \n",
              "mean             172.680946          172.838928                  0.914899   \n",
              "std              103.381604          116.125667                  0.240310   \n",
              "min                0.007000            0.003100                  0.070700   \n",
              "25%               82.248100           62.030375                  0.776725   \n",
              "50%              172.180350          167.750600                  0.965800   \n",
              "75%              253.664375          281.445200                  1.080300   \n",
              "max              359.998100          359.998200                  1.300000   \n",
              "\n",
              "       Aphelion Distance (AU)  Orbital Period (yr)  \\\n",
              "count            15634.000000         15634.000000   \n",
              "mean                 2.647120             2.473433   \n",
              "std                  1.142688             1.517633   \n",
              "min                  0.800000             0.410000   \n",
              "25%                  1.740000             1.520000   \n",
              "50%                  2.520000             2.290000   \n",
              "75%                  3.420000             3.280000   \n",
              "max                 41.540000            98.970000   \n",
              "\n",
              "       Minimum Orbit Intersection Distance (AU)  Orbital Reference  \\\n",
              "count                              15634.000000       15634.000000   \n",
              "mean                                   0.101881          21.886210   \n",
              "std                                    0.105940          33.632956   \n",
              "min                                    0.000000           1.000000   \n",
              "25%                                    0.018700           6.000000   \n",
              "50%                                    0.062000          11.000000   \n",
              "75%                                    0.159175          23.000000   \n",
              "max                                    0.706900         635.000000   \n",
              "\n",
              "       Asteroid Magnitude  Classification  \n",
              "count        15634.000000    15634.000000  \n",
              "mean            22.286336        0.690290  \n",
              "std              3.016002        0.605017  \n",
              "min              9.450000        0.000000  \n",
              "25%             19.900000        0.000000  \n",
              "50%             22.200000        1.000000  \n",
              "75%             24.600000        1.000000  \n",
              "max             33.200000        3.000000  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amXTvZjGACD4"
      },
      "source": [
        "<h2> Data Splitting and Processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gm6bl1w5ACD4"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kws2tkxVACD4"
      },
      "source": [
        "<h3><u> Scaling data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iOTdp7tzACD4"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "X_scaled = pd.DataFrame(X_scaled, columns=X.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWsKmyFTACD4"
      },
      "source": [
        "<h3><u> Splitting data for training and testing\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NiX3jJUvACD4"
      },
      "outputs": [],
      "source": [
        "X_train,X_test, y_train , y_test = train_test_split(X_scaled,y, test_size=0.3,random_state=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wa4bvKR8ACD5"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hvdpi80lACD5"
      },
      "source": [
        "<h2> Model Training and Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXev7q0DACD5"
      },
      "source": [
        "<h3><u> Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u6GYwAziACD5",
        "outputId": "de315864-48bc-4bcb-b535-ee63db822751"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9552334257088041\n"
          ]
        }
      ],
      "source": [
        "model1 = LogisticRegression(max_iter=1000)\n",
        "model1.fit(X_train,y_train)\n",
        "y_pred1 = model1.predict(X_test)\n",
        "model1_accuracy = model1.score(X_test,y_test)\n",
        "print(model1_accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_1Bj1omACD5"
      },
      "source": [
        "<h3><u> Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eha36LLaACD6",
        "outputId": "a3215a0a-9299-4f4e-aba3-340e947238b7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.0\n"
          ]
        }
      ],
      "source": [
        "model2 = DecisionTreeClassifier(random_state=0)\n",
        "model2.fit(X_train,y_train)\n",
        "y_pred2 = model2.predict(X_test)\n",
        "model2_accuracy = model2.score(X_test,y_test)\n",
        "print(model2_accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CgRhZh_qACD6",
        "outputId": "a609ae59-104e-4282-c6be-70947de14ab6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Feature Importances'}>"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "feat_importances = model2.feature_importances_\n",
        "feat_importances = pd.DataFrame(feat_importances, index=X.columns.values)\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.title('Feature Importances')\n",
        "plt.xticks(rotation=90)\n",
        "sns.barplot(data=feat_importances.sort_values(0).T)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gLB9vkOIACD6"
      },
      "outputs": [],
      "source": [
        "import dtreeviz\n",
        "feature_list = X_train.columns\n",
        "viz = dtreeviz.model(\n",
        "    model2,X_train,y_train,target_name='Hazardous', feature_names= feature_list, class_names=['Non-Hazardous','Hazardous']\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_J28sYCcACD6",
        "outputId": "e324e4e4-6f79-4e61-f74d-5b641bf21416"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\aryan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n"
          ]
        },
        {
          "data": {
            "image/svg+xml": [
              "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:ns2=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"350.0\" height=\"463.0\" viewBox=\"0.0 0.0 349.5 463.0\">\n",
              "<g id=\"graph0\" class=\"graph\" transform=\"scale(1.0 1.0) rotate(0) translate(4 459)\">\n",
              "<title>G</title>\n",
              "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-459 345.5,-459 345.5,4 -4,4\" />\n",
              "<g id=\"clust5\" class=\"cluster\"><title>cluster_legend</title>\n",
              "</g>\n",
              "\n",
              "<g id=\"node1\" class=\"node\"><title>node4</title>\n",
              "<svg width=\"147px\" height=\"47px\" viewBox=\"0 0 195.58875 63.05375\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"98.5\" y=\"-110.5\">\n",
              " <metadata>\n",
              "  <rdf:RDF>\n",
              "   <ns2:Work>\n",
              "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
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              "</svg>"
            ],
            "text/plain": [
              "<dtreeviz.utils.DTreeVizRender at 0x217c7a5b6d0>"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "viz.view()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63MUSNdJACD6"
      },
      "source": [
        "<h3><u> Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "skC7Ql4YACD6",
        "outputId": "1c629ba8-6836-4973-bc5a-5210731960ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.0\n"
          ]
        }
      ],
      "source": [
        "model3 = RandomForestClassifier(random_state=0)\n",
        "model3.fit(X_train,y_train)\n",
        "y_pred3 = model3.predict(X_test)\n",
        "model3_accuracy = model3.score(X_test,y_test)\n",
        "print(model3_accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "15_MvpYpACD6",
        "outputId": "f9379a59-c191-4ff7-da36-b60270d70dc2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Feature Importances'}>"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "feat_importances = model3.feature_importances_\n",
        "feat_importances = pd.DataFrame(feat_importances, index=X.columns.values)\n",
        "plt.figure(figsize=(10,5))\n",
        "plt.title('Feature Importances')\n",
        "plt.xticks(rotation=90)\n",
        "sns.barplot(data=feat_importances.sort_values(0).T)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.11"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}